Behavioral Ecology Vol. 7 No. 4: 494-500
Within-bout dynamics of diet choice
Heikki Hirvonen and Esa Ranta
Integrative Ecology Unit, Division of Population Biology, Department of Ecology and Systematics, PO
Box 17, FIN-00014 University of Helsinki, Finland
Conventional diet theories mostly ignore dynamics in prey selectivity during a foraging bout. However, results from experiments
with several aquatic predator species showed that, as more prey were eaten, the predators included more of the initially less
profitable (small) prey types in their diets. We also found that handling times of the initially more profitable (large) prey types
increased with prey sequence, but handling times of the small prey remained constant Consequently, relative profitability of
the large prey declined over the foraging trials. We modeled prey choice by incorporating the change in handling time as a
function of prey sequence. The model predicts a shift in diet as the relative prey values change during a foraging period. The
predictions qualitatively match the empirical data. In addition, simulations over the foraging bout showed that adopting the
strategy using updated profitabilities always gives higher or at least as high total energetic gain as the fixed strategy based on
the classical optimal prey-choice model. These results imply that the predators reevaluate prey profitabilities and adjust their
selectivity accordingly in the course of foraging, without abandoning rate maximization. We suggest that dynamics of diet choice
may in part account for partial preferences frequently observed in studies on prey selection. Key words: foraging behavior,
foraging models, handling time, optimal foraging, partial preferences, prey selection, profitability. [Behav Ecol 7:494—500
(1996)]
S
ince the pioneering endeavors of Emlen (1966) and MacArthur and Pianka (1966), foraging theory has mosdy focused on prey and patch models. The classical model of prey
choice was put forth by Schoener (1971) and Charnov (1976)
(but see also Emlen, 1973; Pulliam, 1974; Werner and Hall,
1974). The model assumptions and predictions have been
summarized by Krebs and McCleery (1984), Stephens and
Krebs (1986), and Krebs and Kacelnik (1991: Table 1). The
classical prey-choice model predicts that the more profitable
prey type should always be accepted upon encounter. Further,
according to the model, the inclusion of low-ranking prey
should not depend on encounter rate, and the inclusion
should be all or nothing. Much empirical work has been done
to verify the prey model (for reviews see Krebs and Kacelnik,
1991; Stephens and Krebs, 1986), but the alk>r-none rule has
not been supported by experimental results.
As the classical prey model is based on Holling's disc equation (Holling, 1959), an implicit assumption is that the net
energy intake per unit handling time (profitability) of a specific prey type is constant (Charnov, 1976; Krebs et al., 1983;
Stephens and Krebs, 1986). Consequently, the classical preychoice model does not predict any change in diet within a
patch and in the course of a foraging bout (Table 1; Krebs et
al., 1983; Stephens and Krebs, 1986). However, there is some
evidence that prey profitabilities change during foraging (e.g.,
Bindoo and Aravindan, 1992; Croy and Hughes, 1991) and
that foraging animals alter their diets within a single foraging
bout (e.g., Confer and O'Bryan, 1989; Godin, 1990; Lucas,
1990). The most common explanation for diet dynamics is
forager satiation, but there are contradictory views about how
diet should change as hunger level declines. For example,
Richards (1983) suggested a model predicting expanding diet
as a forager is near satiation. In contrast, Snyderman (1983)
found that pigeons increased prey selectivity with decreased
deprivation.
According to Pulliam's formulation of the optimal preychoice model (Pulliam, 1974), the diet that maximizes the
Received 7 February 1995; first revision 28 June 1995; second revision 7 December 1995; accepted 20 January 1996.
1045-2249/96/S5.00 O 1996 International Society for Behavioral Ecology
rate of energy intake should be unaffected by predator satiation. However, if relative prey profitabilities change in die
course of foraging, an optimally foraging animal might tend
to shift its diet selection according to these changes. This
could alter prey choice from that predicted by the classical
diet model and would result in apparent partial preferences
(McNamara and Houston, 1987). Unfortunately, most studies
on prey choice report data pooled over the entire foraging
period and do not provide sequential data on handling times
or on diet selection. Therefore, it is impossible to determine
the role of variable prey profitabilities in explaining diet dynamics from published works. Here we examine the consequences of handling time as a function of prey sequence on
prey choice. We assume that a foraging bout is the total
amount of uninterrupted time available for foraging in a
patch of prey with no prey depletion.
We first present empirical data on dynamics of diet selection by four different small aquatic predator species. Invariably, we find that diet width increases toward die end of the
foraging bout and handling time for the larger prey type increases widi the number of that prey type eaten, whereas handling time for die small prey remains unchanged. This observation is then incorporated into a modified diet model assuming that the predators update prey profitability according to
changes in handling times. We dien compare the performance of a forager adopting this strategy widi the performance of a forager using a fixed prey-choice strategy according to the classical prey model and die performance of a strategy based on a truly omniscient predator using a dynamic
decision model.
Empirical observations
Dynamics of diet choice
We used data on prey-size selection by larvae of two anisopteran odonate species (Aeshna juncea and Leucorrhinia dubia), die ten-spined stickleback (Pungitius pungitius), and die
smooth newt (Triturus wdgaris). Diet choice was studied in
experiments widi different prey types available to die predators. To eliminate the effects of prey depletion on prey selection, both prey densities and prey ratios were kept constant
All animals had been deprived of food before die trials. De-
495
Hirvonen and Ranta • Diet dynamics
100
Table 1
Assumptions and predictions of the classical optimal prey-choice
model (after Krebs and McCleery, 1984; Stephens and Krebs, 1986;
Krebs and Kacemik, 1991)
Assumptions
Net energy is a valid measure of prey value
Long-term average rate of energy intake is maximized
Handling time is a fixed constraint
Net energy gain and encounter ate for the tth prey
are fixed and not functions of pt
Energetic costs per unit of handling time are
similar for different prey, i
Handling and searching are mutually exclusive
Prey type, i, is recognizable without errors
Prey are encountered sequentially and randomly
The expected time to find the next prey of type i is
always 1/X,
The forager does not use information it may acquire
while foraging
Predictions
The highest ranking prey, maxte/Aj), should always be
accepted
Low-ranking prey should be ignored according to the
inequality in Equation 2
The exclusion of low-ranking prey is all or nothing
The exclusion of a low-ranking prey does not depend
on the animal's encounter rate, \b with that prey type
60
«j-
Aeshna Leucorrhinia
juncea dubia
11
12
i'
20
100:200
100
0)
a.
E
60
I
20
c
o
c
o
Pungitius pungitius
9 T
"ca
100:200
7x
10
8
iI i
ist
half
2nd
10:10 30:30 50:50100:100
100
QO
60
20
privation time for the odonates was 4 days, for the sticklebacks
12 h, and for the newts, 10 h on average.
We conducted the dragonfly experiments in aquaria containing 1.5 1 of water. Small and large Daphnia magna (mean
lengdis 2.2 mm and 3.8 mm) were offered to the larvae in
constant density and ratio of 200:100. Predation events were
monitored and recorded sequentially. Each trial lasted 30
min. The large prey size was initially the most profitable one
and abundant enough to promote specialization on that prey
(according to the threshold for specialization by the classical
prey model).
We reanalyzed the original data by Ranta and Nuutinen
(1984), who examined prey-size (1.5 mm D. longispina and
2.3 mm D. magna) selection by ten-spined sticklebacks in 40-1
aquaria. Prey were given in 1:1 ratios in four densities (Figure
1). Trials lasting 5 min were used, and prey types eaten were
scored. As Ranta and Nuutinen (1985) examined prey-size selection by adults of the smooth newt in much the same way
(2-1 aquarium, 10-min trials), their data (Figure 1) were also
reanalyzed. We used the original sequential data of prey captures recorded during the foraging bouts. For all the predators used in the experiments, initial prey profitability increased with prey size.
We examined the performance of individual foragers in die
course of the foraging trials. For the analyses, the proportion
of the smaller prey in the foragers' diet was scored before and
after they had eaten half of the prey (= median prey) during
die experimental trial. The data were subjected to a repeatedmeasures ANOVA. During foraging, all the predators examined changed their diet choice (Figure 1), and the ANOVA
showed diat in all cases the time effect (half) is significant
(Table 2). Hence, the proportion of the smaller prey in the
foragers' diet was higher in the second half of all prey eaten
than in the first half (Figure 1).
Handling time
Our observations on prey handling times originate from experiments in which the predators were offered a single prey
5:25 10:2015:15 20:10 25:5
LARGE : SMALL prey
Figure 1
Proportion of small prey (mean with 95% confidence limit) in the
diet of four small aquatic predator species. The total number of
prey eaten is split into two halves: the first half refers to small prey
among the first 50% of all prey eaten, and the second half refers to
small prey among the latter 50% of all prey. For the ten-spined
stickleback, large and small prey were provided in different
densities and for the smooth newt in different ratios (large:small).
Italic numbers refer to sample sizes (for statistical tests, see Table 2).
type. Prey handling times of medium-sized (instar F-2) A. juncea were measured in 30-min experiments (in an aquarium
with 1.5 1 of water) by providing them in separate runs with
100 D. magna of 1.7, 2.7, and 3.8 mm. The larvae were deprived of food for 4 days before die trials. We measured handling time as die time between a capture and termination of
feeding movements of the larval labium. The results clearly
indicate that widi the two largest prey sizes, handling time is
a function of the sequence number of the prey eaten (Figure
2), whereas with the smallest D. magna, diere is no change in
handling time with successive feeds.
With the ten-spined sticklebacks, the prey used were 2.3-mm
D. magna and 1.5-mm D. longispina (in a 5-1 aquarium with
10 prey items). Before the experiments, die sticklebacks were
deprived of food for 12 h. The handling time was measured
from a successful strike until initiation of a search for a new
prey item (Ranta and Nuutinen, 1984).
Because Ranta and Nuutinen (1984) reported only the
mean handling times, we reexamined the original sequential
records of the handling times. Mean handling time of the
larger fish (39 mm) for the first large prey eaten was 4.6 s (SE
= 0.7), and for the sixth item in sequence handling time was
9.5 s (SE = 2.2; paired t test, *,„ = 4.82, p = .0045). The values
for 23-mm fish with large prey were 16.8 s (SE = 1.8) and
Behavioral Ecology Vol. 7 No. 4
496
Table 2
Univariate repeated-measures ANOVA tables of changes in diet
during the foraging trials for the three different types of predators
Source
SS
Odonates
Between subjects
Species
0.159
Error
2.107
Within subjects
Half
0.303
Half*species
0.001
Error
0.800
Ten-spined stickleback
Between subjects
Density5
0.52
Error
1.27
Within subjects
Half*
1.270
Half*density
0.041
Error
1.136
Smooth newt
Between subjects
Ratio"
3.178
Error
3.238
Within subjects
Half*
0.737
Half*ratio
0.111
Error
1.537
Aeshnajuncea
100
df
MS
1
21
0.159
0.100
1.585
.222
1
1
21
0.303
0.001
0.038
7.945
0.014
.010
.905
3
4.10
.015
30 .
0.17
0.04
1
3
30
1.27
0.01
0.03
33.53
0.36
.000
.779
o
CD
W
<D
r
SMALL PREY, y - 17 + 0.1 x.r- 0.09
r
MEDIUM PREY, y - 37+ 1.9 x.r- 0.38
•
•
•
100
i
0
(C)
200
4
38
0.794
0.085
9.322
.000
1
4
38
0.737
0.028
0.040
18.229
0.686
.000
.606
100
LARGE PREY, y = 93 + 8.7 x, r= 0.47
* The response variable (half) is the proportion of smaller prey
(arcsine square-root transformed) in the diet among the first half
and second half of prey eaten.
b
The subjects of density (sticklebacks) and ratio (smooth newt) are
discussed elsewhere (Ranta and Nuutinen, 1984, 1985; Nuutinen
and Ranta, 1986).
5
15
25
Prey sequence
Figure 2
26.6 s (SE = 3.9) for the first and the sixth prey items, respectively (<12 = 2.70, p = .019). No differences in handling
time between the first and the sixth 1.5-mm D. Umgispinawere
observable with the two sizes of stickleback.
We used the original notes by Nuutinen and Ranta (1986)
to score handling times for the first and the eighth prey eaten
by adult female smooth newts. The handling time was defined
as beginning with a successful strike and ending when the
newt's characteristic chewing and swallowing movements
stopped (Nuutinen and Ranta, 1986). The newts were deprived of food for 12 h before the start of the experiments.
With 2.7-mm D. magna, handling times of the first prey item
average 6.0 s (SE = 0.7) and of the eighth item 9.5 s (SE =
0.8). A paired t test indicates that the difference is statistically
significant (tg = 3.61, p = .006). No such change was observed
with 1.7-mm D. magna.
Obviously, handling of the larger prey cannot increase ad
infinitum. We propose that the handling time counter is reset
(e.g., when the gut contents are digested).
Foraging strategies
In this section we describe properties of three different preyselection strategies and their predictions about diet dynamics
during a foraging bout in a patch with two prey types. We
dien compare performance of these strategies in terms of energy intake and diet dynamics over simulated foraging bouts.
Predators using any of these strategies are assumed to make
prey-choice decisions that maximize the amount of energy
gained in foraging time. The differences in some of the as-
Handling time as a function of the sequence number of prey eaten
by F-2 larvae of the odonate Acshna juncta. Three different prey
sizes are graphed separately, (a) 1.7-mm, (b) 2.7-mm, and (c)
3.8-mm D. magna. The insets give the regression line y = a + bx
parameters (indicated with broken line whenever the slope, b,
deviates from zero), together with the Pearson correlation
coefficient, r.
sumptions and properties of the strategies lead to divergent
results in foraging performance.
Fixed strategy
Consider, according to the classical prey-choice model (e.g.,
Stephens and Krebs, 1986), a predator living in an environment with two prey types, with net energy values e, and e%,
encountered at rates X, and X2 per unit time during a total
time, T, spent foraging. We assume diat «, and ^ already include the energetic cost the forager pays for handling these
prey types. The handling times for the two prey types are A,
and Aj, respectively. The profitabilities (<i/A, > <^/Aj) describe the ratios of energy gained per attack to the handling
time per attack by the predator while eating either of the two
prey types. The overall rate of net energy intake of an unselective predator is
(1)
T 1 + X,A, +
which for an energy maximizer says that specialization in prey
type 1 pays if
497
Hirvonen and Rania • Diet dynamics
X,
(2)
«;
That is, the energy gain per foraging time is maximized when
the rate of net energy gain from the more profitable prey type
alone is greater than that derived by eating both prey types.
Equation 2 gives, in terms of time to find the next prey item
of type 1, a threshold value for specialization.
A forager adopting this strategy would behave in accordance with the classical prey model (Table 1), obeying Equation 2, and hence maximizing the long-term average rate of
energy intake during foraging (Charnov, 1976; Gilliam et al.,
1982; Stephens and Charnov, 1982; Stephens and Krebs,
1986). The only factor the predator can control is whether to
attack an item of prey type i (Charnov, 1976). The classical
prey-choice model (Table 1) assumes that a forager following
a decision policy according to Equation 2 should not take into
account the actual changes in prey profitability during foraging. In other words, this forager uses the handling time of
the first prey of each prey type as an estimate of prey handling
time over the bout. Everything else being equal, a forager
adopting this strategy should not change its selectivity over
the bout, and fixed diet choice is predicted. We call this strategy ERIC.
Strategy using acquired information
Modeling the strategy using acquired information is based on
the properties of the classical prey model. However, changes
in handling time, as observed in real foraging events, are now
included. To formulate a model of prey choice according to
the observations on variable handling time supposes that
some of the assumptions of the classical prey model (cf. Table
1) have to be modified. First, handling time of a given prey
type is allowed to change as a function of the number of this
prey type eaten. Second, predators can use the information
they gather from their own performance and act on the consequences for prey profitability in the course of foraging.
With these modifications, analogically to Equation 2, the
rule for specialization of this strategy, ELMO, is
1 <
_
MB|)>
(3)
relative prey profitability) in the course of foraging. It will use
this information to decide whether to attack a prey upon encountering it or wait for the next prey in order to maximize
the net amount of energy gained in the total time spent foraging.
Suppose the forager is about to feed on an encountered
prey, knowing its handling time and the time to the next prey
it would encounter. The predator using ALEX can either skip
the current prey or capture and eat it When doing the latter,
the forager pays the cost of losing the next prey item it may
encounter if it were not engaged in handling the current one,
provided the handling time of the current prey is longer than
the time to encounter the next item. That is, because the
predator is handling the current prey, the predator does not
have the option of capturing the new prey encountered.
Foraging by ALEX can be seen as a decision tree (e.g.,
French, 1989; Taha, 1992). Upon encounter of the first prey,
the forager can either accept the prey item or reject it When
rejecting, the time to the next branching point is the time to
the next encounter. With this second prey a new accept/reject
decision is made. However, if the forager decides to accept
the first prey encountered, the length of the next branch is
the time to the next entry of a prey in the predator's reactive
field only if this time is longer than the handling time of the
current prey. If one or more prey items enter the reactive field
while the predator is still handling the current prey, these new
items will be lost. Then the branch length extends to the time
of the first encounter after handling of the current prey has
terminated. The next decision is then based on the updated
handling time of the prey type last eaten. Since the first encounter the forager has a large number of branching decisions to be made. The best sequence of decisions is the one
yielding the highest energy gain during the entire foraging
time. We assume ALEX can compare the alternatives with true
parameters of each prey item to make these decisions. The
strategy ALEX was implemented by using the dynamic modeling principles in decision analysis (e.g., French, 1989). Note
that ALEX is not a stochastic dynamic programming model
(e.g., Godin, 1990; Hart, 1994; Hart and Gill, 1993; Mangel
and Clark, 1988), but a dynamic decision model (French,
1989; Taha, 1992).
X,
where /»,(n,) indicates that handling time for prey type i is a
function of its sequence number eaten. For example, the
function h,(n,) can be a linear equation (Figure 2), where the
constant (plus slope) is the handling time for the first prey
and the slope is the increase of handling time with subsequent
prey items of this type eaten.
The strategy ELMO satisfies the rule set by Equation 3,
which is a logical extension of Equation 2. If, as was shown
above, the handling time for the initially more profitable prey
type, A,, increases with the number of this prey type eaten, n,,
within a bout, the value of the right-hand side of Equation 3
decreases. If the encounter rate, X,, for the prey type 1 does
not increase, at some n, it is energetically more rewarding for
the predator to eat both prey types indiscriminately. This is
because updating handling times of the two prey types according to their number eaten continuously updates the
threshold value in Equation 3. A separate counter runs for
both prey types.
Dynamic decision strategy using acquired information
The forager using this strategy, ALEX, is assumed to be a truly
omniscient predator and thus to have complete information
on type, encounter rate, and handling time for each prey item
it will encounter during a foraging bout. Note that a predator
adopting the ALEX strategy takes into account the possible
changes in handling time (and consequently the changes in
Simulations
We examined the overall performance of the foraging strategies (ERIC, ELMO, ALEX) with simulations. In our simulations,
two prey types (X, = 0.4, e, = 6, A, = 2, and X2 = 0.25, «2 = 1
and A, = 1; with these realistic average values, the inequality of
Equation 2 of the classical prey' model holds) were randomly
allocated (using the X,) over a foraging period of 200 time
units. The performance of the three strategies in terms of cumulative energy gain and dynamics of diet choice during the
foraging time were tested in this prey environment.
RESULTS
When the handling time for the prey type 1 is not allowed to
change through the foraging time (increase in A, with prey
sequence is 0%), the three strategies fare equally well (Figure
3). However, including lengthening of the handling time for
the prey type 1 as a function of the number of prey of that type
eaten gradually devalues the profitability of this prey type. As
the predator using the dynamic decision strategy ALEX knows
the properties of the prey item it has just encountered and
those of the prey it would encounter while handling the first
one (if it is more profitable to take that one than to wait for
the next one), it always gains more than predators adopting
either of the two other strategies. The strategy ELMO, which
takes into account the changes in handling time, gives a some-
Behavioral Ecology Vol. 7 No. 4
498
100
0%, 120
Eric.
Elmo
80
60
40
ulativega
20
Figure 3
Simulated examples of cumulative energy gain as a function of
elapsing foraging time for the
three different prey-choice
strategies
(ALEX, ELMO,
ERIC; see text for details). The
percentages give the rate of increase in handling time of the
prey type 1 as a function of the
number of this prey type eaten.
Handling time of prey type 2 is
constant. The maximum cumulative energy gain by the
three strategies is indicated by
italic numbers (ALEX, ELMO,
and ERIC are scaled to this value).
E
d
0
100
80
60
40
20
0
0
50
100 150 200
50
100 150 200
Time
what higher cumulative energetic gain than the fixed strategy,
ERIC (Figure 3). This is because, at a given point, according
to Equation 3, diet specialization no more pays for the ELMO
predator adjusting its selectivity as relative prey profitabilities
change, and it shifts to eating the two prey types indiscriminately. From the shifting point onward, this strategy gives a
higher rate of intake than sticking to the fixed choice. Note
that for all the three strategies, the rate of intake declines as
handling time for the prey type 1 increases (Figure 3).
To examine the dynamics of prey selection, we again split
the foraging bout into two halves according to the median
prey eaten. The proportion of the prey type 2 in die predator's diet in both halves was then scored for the adjusted selectivity strategy, ELMO, and for the dynamic decision strateTable 3
Diet dynamics of two foraging strategies (ELMO, ALEX) over
simulated (100 simulations) foraging bouts
5%
gy, ALEX. Recall that according to Equation 2, no changes in
prey selection of the forager adopting thefixedstrategy, ERIC,
are expected. We used the same parameter values reported
above in the description of the simulations for energy intake.
For each combination the simulations were run 100 times.
When the handling times are constant, the adjusted strategy
(ELMO) is specializing in the better prey type over the trial
(Table 3). As handling time for the prey type 1 increases with
consecutive feeds of this prey type, the predator using the
ELMO strategy will switch from a diet specialist to a diet generalist during the foraging bout, and the proportion of the
small prey in its diet increases in the second half of the bout
(Table 3). As the increase in handling time steepens, the shifting point moves toward the beginning of the bout, and the
difference in diet composition between the two halves eventually vanishes (Table 3). The dynamic decision strategy,
ALEX, on the other hand, shows no change in diet, irrespective of the slope of increase in hx (Table 3).
DISCUSSION
Lengthening of A, with n,
0%
0
15%
10%
Behavioral mechanisms of diet dynamics
Strategy
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
ELMO
ALEX
0
52
0
53
5
53
35
54
23
54
40
53
30
56
36
53
The figures are average proportions (%) of the smaller (initial!)' less
profitable) prey type in the forager diets in the first and the second
half of the prey eaten during the bouts. Results are from
simulations with four different slopes (%) of increase in handling
time, A], of the large (initially more profitable) prey type as a
function of prey sequence, n, (see text for details).
We found that all the four small aquatic predator species
changed their diet toward the end of the foraging bout by
including more of the initially less profitable prey type. These
observations are consistent with those of Confer and O'Bryan
(1989) on small rainbow trout (Oncorhyncus irtykiss) and yellow perch (Percaflavescens)feeding on zooplankton. As our
simulations demonstrated, within-bout broadening of the diet
was only predicted by the strategy ELMO, in which prey
choice was based on updated prey profitabilities. The truly
omniscient strategy (ALEX), also using the knowledge of the
changes in handling time, did not show shifts in prey selection
499
Hirvonen and Ranta • Diet dynamics
because it could anticipate the forthcoming prey and therefore could choose the ideal composition of prey items from
those it would encounter during the foraging period. For a
real predator, this is hardly possible. Thus, our results suggest
that predators reevaluate profitabilities of prey types during
foraging and adjust their prey choice accordingly.
This conclusion is substantiated by experimental evidence
that shows animals can assess parameters included in foraging
models (e.g., Charnov, 1976; Jaeger et al., 1982; Shetdeworth
and Plowright, 1992; Stephens et al., 1986). In addition, foragers are clearly capable of updating their diet-choice decisions (Cudiill et al., 1990), even over surprisingly short time
scales (Jaeger et al., 1982; Lucas, 1987b, 1990).
We suppose that widiin-bout changes in relative prey profitability and in diet choice are rather general, but they have
often been overlooked. Many foraging studies on planktivorous fish, for example, have used die first short (< 2 min)
feeding bursts to rank prey and/or test prey preference (e.g.,
Bence and Murdoch, 1986; Buder and Bence, 1984; Li et al.,
1985; Werner and Hall, 1974; but see Confer and O'Bryan,
1989, for an exception). Disregarding widiin-bout changes in
foraging could have been due to, among other things, clinging tighdy to the classical prey model. Experiments testing the
predictions of die classical model have often been of relatively
short duration, and thus changes in diet composition due to
increased handling time, for example, could have been unnoticed. For example, Krebs et al. (1977) truncated die data
on die foraging trials widi great tits after 10 prey items were
eaten (< 300 s) because handling time of die large prey tended to increase after diat and dierefore would have violated
one basic assumption of die classical prey-choice model. If
foragers change dieir selectivity during foraging, as we have
demonstrated, pooling data on prey selection over foraging
time may show apparent partial preferences, as has been demonstrated in odier empirical (Godin, 1990) and dieoretical
(Lucas, 1983, 1985; Lucas and Schmid-Hempel, 1988) examinations. Thus, widiin-bout dynamics of diet selection could
in part explain why partial preferences are so often observed
in studies on prey choice (Krebs and McCleery, 1984; McNamara and Houston, 1987; Stephens and Krebs, 1986).
A variety of alternative foraging models have been used to
predict dynamics in diet selection. However, a common property of diese models is diat die predictions only apply in certain
conditions defined by tight assumptions. For example, foraging
under time constraints has been suggested to prompt die forager to switch from a specialist to a generalist diet at die end
of die bout (Lucas, 1987a,b; Yoerg and Kamil, 1988), but only
if animals are trained to predict die bout lengdi (Lucas, 1990).
Lucas and Schmid-Hempel (1988) showed diat diet widdi
should increase with die time die forager has been in a depleting patch, whereas in nondepleting patches, diet widdi
should decrease widi decreasing time left in die patch. Some
theoretical explorations and empirical tests suggest that diet
dynamics is due to changes in a forager's hunger level, but
disagree whedier diet widdi should increase (e.g., Barnard and
Hurst, 1987; Godin, 1990; Richards, 1983) or decrease (e.g.,
Hart and Ison, 1991; Lucas et al., 1993; Schoener, 1971; Snyderman, 1983) widi satiation. It follows diat die direction of
die change in selectivity during a foraging bout cannot be derived on die mere basis of satiation. In addition to satiation,
prey size relative to predator size and die internal state of die
predator may be important factors in determining prey choice
(Hart, 1994; Hart and Gill, 1993). Furthermore, according to
die prediction of die modified model, ELMO, die direction of
die switch in diet would depend on die actual relative profitabilities of die given prey types, not on satiation as such.
Following Pulliam's idea (Pulliam, 1974), alterations in diet
choice have recendy been explained by models based on state-
dependent decisions of animals balancing energy intake and
odier demands (Godin, 1990; Hart, 1994; Houston, 1993;
Houston and McNamara, 1985; Sih, 1993). If diet-choice decisions affect die risk of predation, foragers should take more
risks when hungry, but choose less risky options as diey near
satiation (McNamara, 1990). For example, diet width of die
guppies studied by Godin (1990) increased widi duration of
die foraging bout Large Daphnia, which took longer to handle, were preferred at die beginning of die foraging trials, but
as more prey were eaten, die guppies shifted dieir diet, and
die diree prey types became equally likely to be eaten. The
result is in accordance widi our observations. [According to
Godin (Godin J-GJ, personal communication), handling times
of large- and medium-sized prey tended to increase widi increasing number of prey ingested during die foraging bouts,
dius matching our observations.] Godin (1990), however, suggested die change in die guppies' diet to be a consequence of
a state-dependent shift in the optimization criterion from energy maximization to minimization of predation risk, diough
no predators were present In contrast to state-dependent models, our modified model (ELMO) demonstrates diat in many
situations no change in die assumptions of optimization currency is required to explain dynamics of optimal diets.
Variable handling time and prey choice
Our findings support die reports suggesting diat lengdiening
of handling time is more pronounced as prey size increases
in relation to predator size (Bindoo and Aravindan, 1992; Kislalioglu and Gibson, 1976; Krebs et al., 1977; Smidi and Dawkins, 1971). This would cause a gradual decline in profitability
of die larger prey. Because handling time did not seem to be
under die foragers' control, we assumed it as a constraint The
exact mechanism causing die increase in handling time in die
course of foraging is not known. It has often been related to
satiation effects, but our results indicate diat satiation alone
cannot account for diis phenomenon.
However, die interpretation depends on how satiation is defined. For example, widi die odonates handling time for die
large Daphnia increases after only a few items have been eaten,
aldiough die predator was prone to eat a much larger number
of prey (see Figure 2). Similarly, for die large ten-spined sticklebacks, it took double die time to handle die sixth large Daphnia compared to die first one, diough sticklebacks are capable
of eating many more prey (Ranta and Nuutinen, 1984). The
same holds for die smooth newt (Nuutinen and Ranta, 1986;
Ranta and Nuutinen, 1985). Thus, it seems diat handling times
for die large prey started to increase when die foragers had
eaten only a negligible portion of dieir capacity.
Our findings are supported by Confer and O'Bryan (1989),
who reported diat small rainbow trout switched prey preference when dieir guts were about 9%-25% full. Similarly, Lucas
(1987a) found diat diet shift of great tits widiin die foraging
bout was unlikely to be due to satiation effects. Furthermore,
odier evidence indicates diat large prey could be more difficult
to process and ingest dian small prey (Bence and Murdoch,
1986; Confer and O'Bryan, 1989; Ernsting and van der Werf,
1988; Kislalioglu and Gibson, 1976). The lengdiening of handling time and its dependence on prey size could also relate to
feedback mechanisms from the gut (Johnson et al., 1975) or
to stomach packing and gut fullness (Hart and Gill, 1992).
Regardless of die underlying mechanisms, change in handling time of a prey type has obvious consequences on diet
choice. By specifying die assumptions of die classical preychoice model and re-forming it by incorporating changes in
prey profitability widiin die foraging bout, we could better
predict how die predators choose dieir diet
500
We thank Jean-Guy Godin for discussions on the topic and for providing us with unpublished information of his experiments on the
diet of the guppy. Comments by Veijo Kaitala, Anssi Laurila, Nina
Peuhkuri, Paul Schmid-Hempel, and anonymous referees on earlier
drafts helped us to improve this paper. We also thank David Stephens
for discussions and appreciate Alejandro Kacelnik's comments and
advice. The experimental work was done at the Tvarminne Zoological
Station, University of Helsinki. The study was supported by the Academy of Finland and the Finnish Cultural Foundation.
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